



'''simple docstring'''






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 				a__(   lowerCamelCase__						,					lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       AutoencoderKL
    lowercase__					=       """sample"""
    lowercase__					=       1E-2







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   str	  =	4
      a		:   Any	  =	3
      a		:   Tuple	  =	(32, 32)

      a		:   str	  =	floats_tensor((batch_size, num_channels) + sizes      ).to(__snake_case      )

      return {"sample": image}







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      return (3, 32, 32)







    @property
    def      lowercase_			( self					:    List[Any]      ):
      return (3, 32, 32)







    def      lowercase_			( self					:    str      ):
      a		:   Dict	  =	{
          'block_out_channels': [32, 64],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 4,
      }
      a		:   Union[str, Any]	  =	self.dummy_input
      return init_dict, inputs_dict







    def      lowercase_			( self					:    str      ):
      pass







    def      lowercase_			( self					:    Optional[int]      ):
      pass







    @unittest.skipIf(torch_device == 'mps'		,     'Gradient checkpointing skipped on MPS'      )
    def      lowercase_			( self					:    List[Any]      ):
      # enable deterministic behavior for gradient checkpointing
      a  ,      a		:   str	  =	self.prepare_init_args_and_inputs_for_common()
      a		:   int	  =	self.model_class(**__snake_case      )
      model.to(__snake_case      )

      assert not model.is_gradient_checkpointing and model.training

      a		:   List[Any]	  =	model(**__snake_case      ).sample
      # run the backwards pass on the model. For backwards pass, for simplicity purpose,
      # we won't calculate the loss and rather backprop on out.sum()
      model.zero_grad()

      a		:   Union[str, Any]	  =	torch.randn_like(__snake_case      )
      a		:   int	  =	(out - labels).mean()
      loss.backward()

      # re-instantiate the model now enabling gradient checkpointing
      a		:   str	  =	self.model_class(**__snake_case      )
      # clone model
      model_a.load_state_dict(model.state_dict()      )
      model_a.to(__snake_case      )
      model_a.enable_gradient_checkpointing()

      assert model_a.is_gradient_checkpointing and model_a.training

      a		:   Optional[int]	  =	model_a(**__snake_case      ).sample
      # run the backwards pass on the model. For backwards pass, for simplicity purpose,
      # we won't calculate the loss and rather backprop on out.sum()
      model_a.zero_grad()
      a		:   int	  =	(out_a - labels).mean()
      loss_a.backward()

      # compare the output and parameters gradients
      self.assertTrue((loss - loss_a).abs() < 1e-5      )
      a		:   Tuple	  =	dict(model.named_parameters()      )
      a		:   List[Any]	  =	dict(model_a.named_parameters()      )
      for name, param in named_params.items():
        self.assertTrue(torch_all_close(param.grad.data		,     named_params_a[name].grad.data		,     atol=5e-5      )      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      a  ,      a		:   Dict	  =	AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'		,     output_loading_info=__snake_case      )
      self.assertIsNotNone(__snake_case      )
      self.assertEqual(len(loading_info['missing_keys']      )		,     0      )

      model.to(__snake_case      )
      a		:   List[Any]	  =	model(**self.dummy_input      )

      assert image is not None, "Make sure output is not None"







    def      lowercase_			( self					:    Tuple      ):
      a		:   List[Any]	  =	AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'      )
      a		:   int	  =	model.to(__snake_case      )
      model.eval()

      if torch_device == "mps":
        a		:   Any	  =	torch.manual_seed(0      )
      else:
        a		:   str	  =	torch.Generator(device=__snake_case      ).manual_seed(0      )

      a		:   Optional[Any]	  =	torch.randn(
          1		,     model.config.in_channels		,     model.config.sample_size		,     model.config.sample_size		,     generator=torch.manual_seed(0      )		,     )
      a		:   List[Any]	  =	image.to(__snake_case      )
      with torch.no_grad():
        a		:   Any	  =	model(__snake_case		,     sample_posterior=__snake_case		,     generator=__snake_case      ).sample

      a		:   List[Any]	  =	output[0, -1, -3:, -3:].flatten().cpu()

      # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
      # the expected output slices are not the same for CPU and GPU.
      if torch_device == "mps":
        a		:   List[str]	  =	torch.tensor(
            [
                -4.0_0_7_8e-0_1,
                -3.8_3_2_3e-0_4,
                -1.2_6_8_1e-0_1,
                -1.1_4_6_2e-0_1,
                2.0_0_9_5e-0_1,
                1.0_8_9_3e-0_1,
                -8.8_2_4_7e-0_2,
                -3.0_3_6_1e-0_1,
                -9.8_6_4_4e-0_3,
            ]      )
      elif torch_device == "cpu":
        a		:   List[Any]	  =	torch.tensor(
            [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]      )
      else:
        a		:   List[Any]	  =	torch.tensor(
            [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]      )

      self.assertTrue(torch_all_close(__snake_case		,     __snake_case		,     rtol=1e-2      )      )

@slow
class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Dict		,     __snake_case					:    List[Any]		,     __snake_case					:    str      ):
      return F"""gaussian_noise_s={seed}_shape={"_".join([str(__snake_case      ) for s in shape]      )}.npy"""







    def      lowercase_			( self					:    List[str]      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    Tuple=0		,     __snake_case					:    int=(4, 3, 5_12, 5_12)		,     __snake_case					:    Any=False      ):
      a		:   Union[str, Any]	  =	torch.floataa if fpaa else torch.floataa
      a		:   str	  =	torch.from_numpy(load_hf_numpy(self.get_file_format(__snake_case		,     __snake_case      )      )      ).to(__snake_case      ).to(__snake_case      )
      return image







    def      lowercase_			( self					:    str		,     __snake_case					:    List[str]="CompVis/stable-diffusion-v1-4"		,     __snake_case					:    int=False      ):
      a		:   List[Any]	  =	'fp16' if fpaa else None
      a		:   Tuple	  =	torch.floataa if fpaa else torch.floataa

      a		:   Dict	  =	AutoencoderKL.from_pretrained(
          __snake_case		,     subfolder='vae'		,     torch_dtype=__snake_case		,     revision=__snake_case		,     )
      model.to(__snake_case      ).eval()

      return model







    def      lowercase_			( self					:    Dict		,     __snake_case					:    List[str]=0      ):
      if torch_device == "mps":
        return torch.manual_seed(__snake_case      )
      return torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]      )
    def      lowercase_			( self					:    Tuple		,     __snake_case					:    Tuple		,     __snake_case					:    Tuple		,     __snake_case					:    str      ):
      a		:   Optional[Any]	  =	self.get_sd_vae_model()
      a		:   List[Any]	  =	self.get_sd_image(__snake_case      )
      a		:   Union[str, Any]	  =	self.get_generator(__snake_case      )

      with torch.no_grad():
        a		:   Union[str, Any]	  =	model(__snake_case		,     generator=__snake_case		,     sample_posterior=__snake_case      ).sample

      assert sample.shape == image.shape

      a		:   str	  =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
      a		:   Tuple	  =	torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice      )

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=3e-3      )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
            # fmt: on
        ]      )
    @require_torch_gpu
    def      lowercase_			( self					:    Any		,     __snake_case					:    Any		,     __snake_case					:    Any      ):
      a		:   List[Any]	  =	self.get_sd_vae_model(fpaa=__snake_case      )
      a		:   List[Any]	  =	self.get_sd_image(__snake_case		,     fpaa=__snake_case      )
      a		:   Tuple	  =	self.get_generator(__snake_case      )

      with torch.no_grad():
        a		:   List[Any]	  =	model(__snake_case		,     generator=__snake_case		,     sample_posterior=__snake_case      ).sample

      assert sample.shape == image.shape

      a		:   Dict	  =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
      a		:   List[str]	  =	torch.tensor(__snake_case      )

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=1e-2      )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]      )
    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    Dict		,     __snake_case					:    Any		,     __snake_case					:    Any      ):
      a		:   int	  =	self.get_sd_vae_model()
      a		:   Tuple	  =	self.get_sd_image(__snake_case      )

      with torch.no_grad():
        a		:   Any	  =	model(__snake_case      ).sample

      assert sample.shape == image.shape

      a		:   Any	  =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
      a		:   Tuple	  =	torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice      )

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=3e-3      )







    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
            # fmt: on
        ]      )
    @require_torch_gpu
    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    Tuple		,     __snake_case					:    Optional[int]      ):
      a		:   int	  =	self.get_sd_vae_model()
      a		:   int	  =	self.get_sd_image(__snake_case		,     shape=(3, 4, 64, 64)      )

      with torch.no_grad():
        a		:   Dict	  =	model.decode(__snake_case      ).sample

      assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

      a		:   Tuple	  =	sample[-1, -2:, :2, -2:].flatten().cpu()
      a		:   List[str]	  =	torch.tensor(__snake_case      )

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=1e-3      )







    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
            # fmt: on
        ]      )
    @require_torch_gpu
    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    List[str]		,     __snake_case					:    List[str]      ):
      a		:   int	  =	self.get_sd_vae_model(fpaa=__snake_case      )
      a		:   List[str]	  =	self.get_sd_image(__snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=__snake_case      )

      with torch.no_grad():
        a		:   Tuple	  =	model.decode(__snake_case      ).sample

      assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

      a		:   Tuple	  =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
      a		:   List[str]	  =	torch.tensor(__snake_case      )

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=5e-3      )







    @parameterized.expand([(13,), (16,), (27,)]      )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()		,     reason='xformers is not required when using PyTorch 2.0.'      )
    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Optional[Any]      ):
      a		:   str	  =	self.get_sd_vae_model(fpaa=__snake_case      )
      a		:   List[str]	  =	self.get_sd_image(__snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=__snake_case      )

      with torch.no_grad():
        a		:   str	  =	model.decode(__snake_case      ).sample

      model.enable_xformers_memory_efficient_attention()
      with torch.no_grad():
        a		:   List[Any]	  =	model.decode(__snake_case      ).sample

      assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=1e-1      )







    @parameterized.expand([(13,), (16,), (37,)]      )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()		,     reason='xformers is not required when using PyTorch 2.0.'      )
    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    str      ):
      a		:   Optional[Any]	  =	self.get_sd_vae_model()
      a		:   Tuple	  =	self.get_sd_image(__snake_case		,     shape=(3, 4, 64, 64)      )

      with torch.no_grad():
        a		:   List[str]	  =	model.decode(__snake_case      ).sample

      model.enable_xformers_memory_efficient_attention()
      with torch.no_grad():
        a		:   Dict	  =	model.decode(__snake_case      ).sample

      assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

      assert torch_all_close(__snake_case		,     __snake_case		,     atol=1e-2      )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
            # fmt: on
        ]      )
    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    str		,     __snake_case					:    Union[str, Any]      ):
      a		:   Dict	  =	self.get_sd_vae_model()
      a		:   str	  =	self.get_sd_image(__snake_case      )
      a		:   int	  =	self.get_generator(__snake_case      )

      with torch.no_grad():
        a		:   Optional[Any]	  =	model.encode(__snake_case      ).latent_dist
        a		:   Any	  =	dist.sample(generator=__snake_case      )

      assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

      a		:   str	  =	sample[0, -1, -3:, -3:].flatten().cpu()
      a		:   int	  =	torch.tensor(__snake_case      )

      a		:   Optional[Any]	  =	3e-3 if torch_device != 'mps' else 1e-2
      assert torch_all_close(__snake_case		,     __snake_case		,     atol=__snake_case      )




'''simple docstring'''






import numpy as np
def  lowerCamelCase__			(		_A							):
  return 1 / (1 + np.exp(-vector							))
def  lowerCamelCase__			(		_A							):
  return vector * sigmoid(1.702 * vector							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()